Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	142,207
2	Rhode Island	141,195
3	South Dakota	139,300
4	Utah	124,713
5	Tennessee	122,704
6	Arizona	119,175
7	Iowa	116,414
8	Nebraska	114,325
9	Wisconsin	114,285
10	Oklahoma	113,634
11	New Jersey	113,362
12	South Carolina	113,304
13	Arkansas	111,726
14	Delaware	108,624
15	Indiana	108,595
16	Alabama	108,159
17	Kansas	107,313
18	Illinois	106,962
19	New York	106,159
20	Idaho	105,679
21	Florida	105,345
22	Mississippi	105,225
23	Minnesota	103,866
24	Nevada	103,235
25	Montana	102,783
26	Georgia	102,080
27	Wyoming	101,290
28	Kentucky	101,048
29	Massachusetts	100,942
30	Texas	100,428
31	Louisiana	99,331
32	Missouri	98,641
33	Connecticut	96,126
34	Michigan	95,979
35	New Mexico	95,064
36	California	95,050
37	North Carolina	94,183
38	Alaska	93,396
39	Ohio	92,523
40	Pennsylvania	91,744
41	Colorado	90,950
42	West Virginia	86,867
43	Virginia	77,948
44	Maryland	74,801
45	New Hampshire	70,851
46	District of Columbia	68,205
47	Washington	54,430
48	Puerto Rico	52,598
49	Maine	47,212
50	Oregon	45,063
51	Vermont	37,489
52	Hawaii	23,626

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Michigan	350
2	Colorado	302
3	Minnesota	256
4	Maine	236
5	Pennsylvania	216
6	West Virginia	209
7	Illinois	203
8	Rhode Island	203
9	Florida	202
10	Delaware	195
11	Oregon	193
12	North Dakota	187
13	New Jersey	184
14	Indiana	175
15	Washington	174
16	North Carolina	171
17	Alaska	164
18	Connecticut	164
19	Massachusetts	152
20	New Hampshire	152
21	Kentucky	148
22	Wyoming	147
23	South Carolina	139
24	Nevada	136
25	Wisconsin	132
26	Montana	131
27	Tennessee	131
28	Utah	131
29	New York	128
30	Iowa	123
31	New Mexico	120
32	Ohio	120
33	Nebraska	115
34	Maryland	111
35	Georgia	108
36	Vermont	107
37	Kansas	105
38	Alabama	104
39	Idaho	102
40	Louisiana	102
41	Virginia	102
42	Missouri	100
43	Arizona	99
44	South Dakota	97
45	Texas	90
46	Puerto Rico	89
47	Arkansas	78
48	Hawaii	75
49	District of Columbia	70
50	Mississippi	62
51	California	57
52	Oklahoma	53

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,901
2	New York	2,676
3	Massachusetts	2,563
4	Rhode Island	2,536
5	Mississippi	2,428
6	Arizona	2,389
7	Connecticut	2,282
8	Louisiana	2,244
9	South Dakota	2,238
10	Alabama	2,236
11	Pennsylvania	2,073
12	North Dakota	2,006
13	Indiana	1,989
14	New Mexico	1,955
15	Illinois	1,935
16	Michigan	1,926
17	Arkansas	1,908
18	Iowa	1,896
19	South Carolina	1,857
20	Georgia	1,855
21	Nevada	1,784
22	Tennessee	1,780
23	Texas	1,750
24	Kansas	1,736
25	Oklahoma	1,726
26	Delaware	1,672
27	Ohio	1,662
28	Florida	1,659
29	California	1,574
30	District of Columbia	1,572
31	Missouri	1,520
32	West Virginia	1,517
33	Kentucky	1,493
34	Montana	1,489
35	Maryland	1,461
36	Wisconsin	1,309
37	Minnesota	1,296
38	Virginia	1,273
39	Nebraska	1,230
40	Wyoming	1,226
41	North Carolina	1,220
42	Idaho	1,154
43	Colorado	1,126
44	New Hampshire	964
45	Puerto Rico	737
46	Washington	736
47	Utah	693
48	Oregon	601
49	Maine	590
50	Alaska	453
51	Vermont	399
52	Hawaii	341

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Michigan	6
2	Montana	4
3	New Jersey	4
4	Pennsylvania	4
5	West Virginia	4
6	Alabama	3
7	Florida	3
8	Kansas	3
9	Oklahoma	3
10	Puerto Rico	3
11	Georgia	2
12	Illinois	2
13	Iowa	2
14	Louisiana	2
15	Minnesota	2
16	New Mexico	2
17	New York	2
18	North Carolina	2
19	Ohio	2
20	South Carolina	2
21	South Dakota	2
22	Texas	2
23	Wisconsin	2
24	Arizona	1
25	California	1
26	Colorado	1
27	Connecticut	1
28	Delaware	1
29	District of Columbia	1
30	Idaho	1
31	Indiana	1
32	Kentucky	1
33	Maryland	1
34	Massachusetts	1
35	Missouri	1
36	Nebraska	1
37	Nevada	1
38	North Dakota	1
39	Oregon	1
40	Rhode Island	1
41	Tennessee	1
42	Virginia	1
43	Washington	1
44	Alaska	0
45	Arkansas	0
46	Hawaii	0
47	Maine	0
48	Mississippi	0
49	New Hampshire	0
50	Utah	0
51	Vermont	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Chattahoochee	Georgia	365,178	1	99
Crowley	Colorado	360,667	2	99
Bent	Colorado	273,982	3	99
Dewey	South Dakota	249,830	4	99
Lincoln	Arkansas	245,700	5	99
Davidson	Tennessee	141,920	186	94
Richland	South Carolina	111,670	983	68
York	South Carolina	110,467	1036	67
Orange	California	85,218	2206	29
Pierce	Washington	56,129	2865	8

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Galax city	Virginia	8,193	2	99
Jerauld	South Dakota	7,948	3	99
Emporia city	Virginia	7,856	4	99
Hancock	Georgia	7,686	5	99
Orange	California	1,574	1817	42
York	South Carolina	1,345	2090	33
Davidson	Tennessee	1,344	2092	33
Richland	South Carolina	1,335	2101	33
Pierce	Washington	724	2745	12

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.